Particular embodiments generally relate to data analysis and more specifically to determining probability distributions or likelihood estimators based on the data and for determining answers to specific questions or features of interest pertaining to the data.
Processes for determining a probability distribution or likelihood estimator descriptive of a given dataset are employed in various demanding applications including analysis of experimental results from scientific studies, determination of probabilities of occurrences of certain events based on observations, and so on. Such applications often demand robust unbiased methods for determining answers to a specific question or feature of interest given a dataset. The dataset may represent experimental results or other observations.
Probability estimation and likelihood estimation are particularly important in biological fields, such as genetics, medicine, and communications systems, where complex problems involving multiple variables are common. Probability estimation often involves employing a probability distribution, also called a probability density function, to determine the probability of occurrence of a certain event or feature. Likelihood estimation involves determining an estimate of a probability distribution, called the density estimator or likelihood estimator based on a predetermined dataset.
Conventionally, global parameters of a distribution, such as the standard deviation and mean of the normal distribution are adjusted to describe the observed data as accurately as possible. Unfortunately, observed data does not always behave according to various well-known distributions. Consequently, such distributions may be particularly inaccurate for certain questions of interest. Thus, accurate estimation of a specified feature of distribution of observed data in large models (e.g., collections of probability distributions that cannot be described by a finite number of parameters) is crucial for the ability to answer questions of interest based on data.